Pricing and distributed QoS control for elastic network traffic

Berg, Hans van den; Mandjes, Michel; Núñez-Queija, Rudesindo

doi:10.1016/j.orl.2006.03.018

Cited by 7 publications

(3 citation statements)

References 26 publications

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“…In [11] the authors design a framework that is composed of feedback signals and the corresponding source adaptation scheme to provide differentiated bandwidth service for elastic and inelastic applications. In [12], authors propose an appropriate prioritization pricing structure where users are provided with incentives and are able to choose between two service classes. In [13], authors present an integrated solution (integrating pricing into QoS routing) for enabling the next generation Internet to achieve the differentiated service and availability guarantee.…”

Section: Analysis Of a Pricing Methods For Elastic Services With Guaramentioning

confidence: 99%

“…Nevertheless, in this work, this approximation offers a more intuitive expression that allows knowing better and quicker its quantitative evaluation and to clarify its qualitative behavior. Figure 11 shows the relative error (higher than 3%) for the calculation of the GoS i using the approximation in (12). The relative error is higher when   …”

Section: A2 Evaluation Of the Gosmentioning

confidence: 99%

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Analysis of a Pricing Method for Elastic Services with Guaranteed GoS

Postigo-Boix¹,

Melús-Moreno²

2012

JSSM

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Service Providers (SPs), which offer services based on elastic reservations with a guaranteed Grade of Service (GoS), should know how to price these services and how to quantify the benefits in different scenarios. This paper analyzes a method for evaluating the price of a service based on elastic reservations with a guaranteed Grade of Service. The method works as follows: First, the SP determines the requirements of the service that wants to offer; Second, the SP evaluates the average rate of the accepted elastic reservations of the service with a guaranteed GoS; Third, the SP calculates the price that guarantees the GoS with an aggregate demand function that depends on a demand modulation factor of the elastic reservations that is the mean reserved bandwidth, B res ; and Finally, the SP obtains the optimum value of the elasticity of the reservations that gives the maximum revenue, and the required access bandwidth in this case. The paper not only applies the method to a class i of elastic reservations when a linear-based demand and a revenue function are selected, but it also analyzes the influence of each one of the considered parameters. This method could be extended to the case of multiple classes of independent and guaranteed elastic services, applying the method to each service with its estimated demand and revenue functions.

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Section: Analysis Of a Pricing Methods For Elastic Services With Guaramentioning

confidence: 99%

Section: A2 Evaluation Of the Gosmentioning

confidence: 99%

Analysis of a Pricing Method for Elastic Services with Guaranteed GoS

Postigo-Boix¹,

Melús-Moreno²

2012

JSSM

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“…Kim and Mannino [17] studied the profit maximization in an M/G/1 queue with non-preemptive priorities and N different classes. Berg, Mandjes and Núñez-Queija [3] dealt with the revenue maximization in an observable M/G/1 queueing system with two classes and two optional priorities. Printezis and Burnetasthe [23] analyzed the optimal option pricing policy in an M/M/m queue, where some priority options were provided for customers.…”

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confidence: 99%

Equilibrium and optimal balking strategies for low-priority customers in the M/G/1 queue with two classes of customers and preemptive priority

Xu¹,

Xu²,

Yao³

2019

Journal of Industrial &Amp; Management Optimization

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This paper investigates the low-priority customers strategic behavior in the single-server queueing system with general service time and two customer types. The priority system is preemptive resume, which means that if a high-priority customer enters the system that are serving a low-priority customer, the arriving customer preempts the service facility and the preempted customer returns to the head of the queue for his own class. The customer who is preempted resumes service at the point of interruption upon reentering the system. The low-priority customers dilemma is whether to join or balk based on a linear reward-cost structure. Two cases are distinguished based on the different levels of information that the low-priority customers acquire before joining the system. The equilibrium threshold strategy in the observable case and the equilibrium balking strategy as well as the socially optimal balking strategy in the unobservable case for the low-priority customers are derived finally.

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Strategic Revenue Management for Discriminatory Processor Sharing Queues

Fiems

2023

Lecture Notes in Computer Science

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Pricing and distributed QoS control for elastic network traffic

Cited by 7 publications

References 26 publications

Analysis of a Pricing Method for Elastic Services with Guaranteed GoS

Analysis of a Pricing Method for Elastic Services with Guaranteed GoS

Equilibrium and optimal balking strategies for low-priority customers in the M/G/1 queue with two classes of customers and preemptive priority

Strategic Revenue Management for Discriminatory Processor Sharing Queues

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